{
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  "metadata": {
    "colab": {
      "name": "transfer-learning.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kylemath/ml4a-guides/blob/master/notebooks/transfer-learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "92mQ2gMlYoZl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Transfer learning / fine-tuning\n",
        "\n",
        "This tutorial will guide you through the process of using _transfer learning_ to learn an accurate image classifier from a relatively small number of training samples. Generally speaking, transfer learning refers to the process of leveraging the knowledge learned in one model for the training of another model. \n",
        "\n",
        "More specifically, the process involves taking an existing neural network which was previously trained to good performance on a larger dataset, and using it as the basis for a new model which leverages that previous network's accuracy for a new task. This method has become popular in recent years to improve the performance of a neural net trained on a small dataset; the intuition is that the new dataset may be too small to train to good performance by itself, but we know that most neural nets trained to learn image features often learn similar features anyway, especially at early layers where they are more generic (edge detectors, blobs, and so on). \n",
        "\n",
        "Transfer learning has been largely enabled by the open-sourcing of state-of-the-art models; for the top performing models in image classification tasks (like from [ILSVRC](http://www.image-net.org/challenges/LSVRC/)), it is common practice now to not only publish the architecture, but to release the trained weights of the model as well. This lets amateurs use these top image classifiers to boost the performance of their own task-specific models.\n",
        "\n",
        "#### Feature extraction vs. fine-tuning\n",
        "\n",
        "At one extreme, transfer learning can involve taking the pre-trained network and freezing the weights, and using one of its hidden layers (usually the last one) as a feature extractor, using those features as the input to a smaller neural net. \n",
        "\n",
        "At the other extreme, we start with the pre-trained network, but we allow some of the weights (usually the last layer or last few layers) to be modified. Another name for this procedure is called \"fine-tuning\" because we are slightly adjusting the pre-trained net's weights to the new task. We usually train such a network with a lower learning rate, since we expect the features are already relatively good and do not need to be changed too much. \n",
        "\n",
        "Sometimes, we do something in-between: Freeze just the early/generic layers, but fine-tune the later layers. Which strategy is best depends on the size of your dataset, the number of classes, and how much it resembles the dataset the previous model was trained on (and thus, whether it can benefit from the same learned feature extractors). A more detailed discussion of how to strategize can be found in [[1]](http://cs231n.github.io/transfer-learning/) [[2]](http://sebastianruder.com/transfer-learning/).\n",
        "\n",
        "## Procedure\n",
        "\n",
        "In this guide will go through the process of loading a state-of-the-art, 1000-class image classifier, [VGG16](https://arxiv.org/pdf/1409.1556.pdf) which [won the ImageNet challenge in 2014](http://www.robots.ox.ac.uk/~vgg/research/very_deep/), and using it as a fixed feature extractor to train a smaller custom classifier on our own images, although with very few code changes, you can try fine-tuning as well.\n",
        "\n",
        "We will first load VGG16 and remove its final layer, the 1000-class softmax classification layer specific to ImageNet, and replace it with a new classification layer for the classes we are training over. We will then freeze all the weights in the network except the new ones connecting to the new classification layer, and then train the new classification layer over our new dataset. \n",
        "\n",
        "We will also compare this method to training a small neural network from scratch on the new dataset, and as we shall see, it will dramatically improve our accuracy. We will do that part first.\n",
        "\n",
        "As our test subject, we'll use a dataset consisting of around 6000 images belonging to 97 classes, and train an image classifier with around 80% accuracy on it. It's worth noting that this strategy scales well to image sets where you may have even just a couple hundred or less images. Its performance will be lesser from a small number of samples (depending on classes) as usual, but still impressive considering the usual constraints.\n"
      ]
    },
    {
      "metadata": {
        "id": "3p-OjhDPYoZm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8240657a-5982-4f20-c272-8f830adae8b2"
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import os\n",
        "\n",
        "#if using Theano with GPU\n",
        "#os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n",
        "\n",
        "import random\n",
        "import numpy as np\n",
        "import keras\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import imshow\n",
        "\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.imagenet_utils import preprocess_input\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Activation\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.models import Model"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "VWWN-FPLYoZs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Getting a dataset\n",
        "\n",
        "The first step is going to be to load our data. As our example, we will be using the dataset [CalTech-101](http://www.vision.caltech.edu/Image_Datasets/Caltech101/), which contains around 9000 labeled images belonging to 101 object categories. However, we will exclude 5 of the categories which have the most images. This is in order to keep the class distribution fairly balanced (around 50-100) and constrained to a smaller number of images, around 6000. \n",
        "\n",
        "To obtain this dataset, you can either run the download script `download.sh` in the `data` folder, or the following commands:\n",
        "\n",
        "    wget http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz\n",
        "    tar -xvzf 101_ObjectCategories.tar.gz\n",
        "\n",
        "If you wish to use your own dataset, it should be aranged in the same fashion to `101_ObjectCategories` with all of the images organized into subfolders, one for each class. In this case, the following cell should load your custom dataset correctly by just replacing `root` with your folder. If you have an alternate structure, you just need to make sure that you load the list `data` where every element is a dict where `x` is the data (a 1-d numpy array) and `y` is the label (an integer). Use the helper function `get_image(path)` to load the image correctly into the array, and note also that the images are being resized to 224x224. This is necessary because the input to VGG16 is a 224x224 RGB image. You do not need to resize them on your hard drive, as that is being done in the code below.\n",
        "\n",
        "If you have `101_ObjectCategories` in your data folder, the following cell should load all the data."
      ]
    },
    {
      "metadata": {
        "id": "XklKIrnaZb3f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "85c8a89c-e882-4e54-c7cf-1466fca5f8d4"
      },
      "cell_type": "code",
      "source": [
        "!echo \"Downloading 101_Object_Categories for image notebooks\"\n",
        "!curl -L -o 101_ObjectCategories.tar.gz --progress-bar http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz\n",
        "!tar -xzf 101_ObjectCategories.tar.gz\n",
        "!rm 101_ObjectCategories.tar.gz\n",
        "!ls\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading 101_Object_Categories for image notebooks\n",
            "######################################################################## 100.0%\n",
            "101_ObjectCategories  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8OGRcLNwYoZu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "c5195ab1-d738-40da-dc08-f147efd43f4e"
      },
      "cell_type": "code",
      "source": [
        "root = '101_ObjectCategories'\n",
        "exclude = ['BACKGROUND_Google', 'Motorbikes', 'airplanes', 'Faces_easy', 'Faces']\n",
        "train_split, val_split = 0.7, 0.15\n",
        "\n",
        "categories = [x[0] for x in os.walk(root) if x[0]][1:]\n",
        "categories = [c for c in categories if c not in [os.path.join(root, e) for e in exclude]]\n",
        "\n",
        "print(categories)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['101_ObjectCategories/grand_piano', '101_ObjectCategories/inline_skate', '101_ObjectCategories/binocular', '101_ObjectCategories/crocodile', '101_ObjectCategories/barrel', '101_ObjectCategories/sunflower', '101_ObjectCategories/wrench', '101_ObjectCategories/pizza', '101_ObjectCategories/pigeon', '101_ObjectCategories/cougar_face', '101_ObjectCategories/dalmatian', '101_ObjectCategories/mandolin', '101_ObjectCategories/scissors', '101_ObjectCategories/panda', '101_ObjectCategories/buddha', '101_ObjectCategories/bass', '101_ObjectCategories/elephant', '101_ObjectCategories/starfish', '101_ObjectCategories/dolphin', '101_ObjectCategories/brain', '101_ObjectCategories/flamingo_head', '101_ObjectCategories/lamp', '101_ObjectCategories/crayfish', '101_ObjectCategories/sea_horse', '101_ObjectCategories/chair', '101_ObjectCategories/ferry', '101_ObjectCategories/wild_cat', '101_ObjectCategories/cellphone', '101_ObjectCategories/hawksbill', '101_ObjectCategories/trilobite', '101_ObjectCategories/wheelchair', '101_ObjectCategories/laptop', '101_ObjectCategories/ketch', '101_ObjectCategories/llama', '101_ObjectCategories/rhino', '101_ObjectCategories/cannon', '101_ObjectCategories/mayfly', '101_ObjectCategories/car_side', '101_ObjectCategories/strawberry', '101_ObjectCategories/minaret', '101_ObjectCategories/stegosaurus', '101_ObjectCategories/okapi', '101_ObjectCategories/garfield', '101_ObjectCategories/crab', '101_ObjectCategories/pagoda', '101_ObjectCategories/metronome', '101_ObjectCategories/gramophone', '101_ObjectCategories/emu', '101_ObjectCategories/octopus', '101_ObjectCategories/pyramid', '101_ObjectCategories/lobster', '101_ObjectCategories/stop_sign', '101_ObjectCategories/anchor', '101_ObjectCategories/helicopter', '101_ObjectCategories/dollar_bill', '101_ObjectCategories/cup', '101_ObjectCategories/beaver', '101_ObjectCategories/hedgehog', '101_ObjectCategories/water_lilly', '101_ObjectCategories/soccer_ball', '101_ObjectCategories/menorah', '101_ObjectCategories/rooster', '101_ObjectCategories/joshua_tree', '101_ObjectCategories/bonsai', '101_ObjectCategories/windsor_chair', '101_ObjectCategories/revolver', '101_ObjectCategories/kangaroo', '101_ObjectCategories/accordion', '101_ObjectCategories/saxophone', '101_ObjectCategories/ibis', '101_ObjectCategories/butterfly', '101_ObjectCategories/camera', '101_ObjectCategories/platypus', '101_ObjectCategories/chandelier', '101_ObjectCategories/scorpion', '101_ObjectCategories/crocodile_head', '101_ObjectCategories/ant', '101_ObjectCategories/electric_guitar', '101_ObjectCategories/cougar_body', '101_ObjectCategories/flamingo', '101_ObjectCategories/snoopy', '101_ObjectCategories/tick', '101_ObjectCategories/schooner', '101_ObjectCategories/dragonfly', '101_ObjectCategories/gerenuk', '101_ObjectCategories/euphonium', '101_ObjectCategories/ceiling_fan', '101_ObjectCategories/ewer', '101_ObjectCategories/stapler', '101_ObjectCategories/umbrella', '101_ObjectCategories/Leopards', '101_ObjectCategories/lotus', '101_ObjectCategories/yin_yang', '101_ObjectCategories/brontosaurus', '101_ObjectCategories/nautilus', '101_ObjectCategories/watch', '101_ObjectCategories/headphone']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "K2ERhVlFYoZy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "This function is useful for pre-processing the data into an image and input vector."
      ]
    },
    {
      "metadata": {
        "id": "A1T1Joq7YoZz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# helper function to load image and return it and input vector\n",
        "def get_image(path):\n",
        "    img = image.load_img(path, target_size=(224, 224))\n",
        "    x = image.img_to_array(img)\n",
        "    x = np.expand_dims(x, axis=0)\n",
        "    x = preprocess_input(x)\n",
        "    return img, x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zUwQ60GGYoZ3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Load all the images from root folder"
      ]
    },
    {
      "metadata": {
        "id": "5nAUr-ooYoZ4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data = []\n",
        "for c, category in enumerate(categories):\n",
        "    images = [os.path.join(dp, f) for dp, dn, filenames \n",
        "              in os.walk(category) for f in filenames \n",
        "              if os.path.splitext(f)[1].lower() in ['.jpg','.png','.jpeg']]\n",
        "    for img_path in images:\n",
        "        img, x = get_image(img_path)\n",
        "        data.append({'x':np.array(x[0]), 'y':c})\n",
        "\n",
        "# count the number of classes\n",
        "num_classes = len(categories)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "55Rw-ptVYoZ7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Randomize the data order."
      ]
    },
    {
      "metadata": {
        "id": "5vGeJK55YoZ8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "random.shuffle(data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OwHqS_NgYoZ_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "create training / validation / test split (70%, 15%, 15%)"
      ]
    },
    {
      "metadata": {
        "id": "PT9Cuq2rYoaB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "idx_val = int(train_split * len(data))\n",
        "idx_test = int((train_split + val_split) * len(data))\n",
        "train = data[:idx_val]\n",
        "val = data[idx_val:idx_test]\n",
        "test = data[idx_test:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EsOVhpqcYoaF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Separate data for labels."
      ]
    },
    {
      "metadata": {
        "id": "vQOGN9kOYoaH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "b992f7bd-aca6-4d4d-e79d-9efaeeb7d2be"
      },
      "cell_type": "code",
      "source": [
        "x_train, y_train = np.array([t[\"x\"] for t in train]), [t[\"y\"] for t in train]\n",
        "x_val, y_val = np.array([t[\"x\"] for t in val]), [t[\"y\"] for t in val]\n",
        "x_test, y_test = np.array([t[\"x\"] for t in test]), [t[\"y\"] for t in test]\n",
        "print(y_test)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[51, 24, 43, 74, 5, 14, 33, 57, 40, 14, 63, 50, 28, 91, 28, 43, 53, 28, 81, 93, 25, 0, 84, 28, 21, 33, 70, 77, 51, 70, 32, 87, 77, 57, 3, 88, 71, 3, 67, 63, 38, 37, 32, 76, 36, 10, 80, 89, 32, 95, 19, 33, 11, 65, 78, 33, 51, 90, 19, 64, 25, 55, 39, 92, 33, 53, 90, 50, 95, 29, 91, 43, 11, 57, 90, 70, 42, 17, 53, 0, 67, 47, 76, 96, 8, 74, 77, 39, 3, 57, 29, 17, 81, 9, 86, 32, 85, 66, 0, 60, 89, 30, 78, 75, 69, 73, 14, 86, 30, 0, 34, 52, 95, 67, 58, 29, 66, 63, 0, 11, 18, 63, 51, 5, 22, 92, 96, 41, 18, 34, 39, 25, 31, 55, 40, 92, 65, 22, 88, 14, 32, 33, 15, 87, 59, 85, 66, 73, 91, 70, 51, 33, 58, 47, 37, 28, 27, 69, 9, 32, 77, 89, 31, 91, 0, 41, 24, 52, 3, 57, 95, 8, 28, 38, 95, 89, 82, 37, 66, 67, 64, 73, 14, 39, 14, 16, 77, 32, 74, 51, 37, 95, 0, 89, 69, 95, 87, 30, 64, 76, 56, 53, 60, 58, 15, 69, 94, 69, 81, 77, 17, 2, 86, 79, 41, 89, 16, 80, 33, 55, 53, 3, 31, 15, 78, 75, 66, 30, 1, 25, 74, 77, 0, 92, 35, 90, 55, 0, 22, 23, 1, 52, 65, 15, 16, 9, 1, 28, 78, 78, 90, 79, 87, 95, 83, 33, 59, 17, 30, 23, 64, 58, 9, 61, 23, 60, 62, 89, 12, 81, 9, 49, 37, 62, 28, 29, 70, 33, 53, 95, 5, 17, 27, 60, 94, 90, 10, 1, 59, 19, 84, 78, 9, 75, 15, 60, 40, 4, 35, 33, 81, 95, 34, 90, 65, 88, 60, 91, 59, 10, 35, 53, 43, 70, 65, 95, 26, 95, 63, 0, 86, 73, 84, 27, 44, 94, 35, 38, 63, 9, 13, 24, 14, 54, 77, 37, 81, 55, 48, 35, 41, 25, 29, 58, 63, 96, 16, 95, 29, 53, 3, 82, 67, 7, 72, 90, 43, 85, 75, 31, 28, 11, 88, 76, 95, 5, 12, 90, 39, 32, 95, 4, 22, 0, 28, 5, 30, 57, 48, 95, 77, 28, 25, 92, 19, 61, 41, 33, 54, 47, 23, 34, 34, 11, 95, 37, 66, 19, 69, 43, 19, 24, 70, 33, 0, 25, 9, 0, 45, 19, 2, 90, 0, 74, 69, 67, 96, 19, 28, 94, 93, 79, 31, 14, 2, 33, 95, 66, 27, 82, 57, 70, 11, 39, 34, 36, 28, 91, 33, 26, 59, 39, 44, 82, 35, 41, 69, 37, 19, 64, 95, 31, 50, 74, 48, 66, 3, 53, 5, 69, 42, 58, 26, 31, 44, 95, 38, 92, 19, 32, 36, 27, 5, 28, 65, 44, 69, 17, 87, 54, 57, 31, 37, 62, 90, 89, 78, 26, 21, 91, 59, 49, 39, 95, 80, 66, 61, 93, 90, 77, 78, 82, 90, 21, 83, 95, 73, 19, 10, 36, 16, 87, 90, 9, 60, 69, 37, 17, 91, 37, 66, 84, 53, 95, 16, 0, 5, 74, 22, 74, 90, 39, 58, 95, 63, 38, 36, 76, 25, 5, 48, 91, 95, 72, 46, 85, 0, 90, 82, 77, 54, 50, 45, 28, 96, 93, 51, 19, 73, 25, 78, 92, 85, 95, 25, 31, 45, 79, 73, 18, 43, 29, 59, 68, 12, 81, 1, 7, 53, 35, 28, 31, 91, 39, 21, 9, 71, 24, 90, 89, 3, 43, 62, 63, 53, 13, 84, 95, 10, 48, 14, 23, 83, 83, 24, 46, 12, 27, 14, 42, 9, 73, 89, 88, 19, 10, 88, 53, 21, 63, 37, 37, 84, 55, 91, 57, 63, 14, 87, 69, 8, 21, 8, 4, 18, 69, 24, 76, 33, 61, 78, 43, 10, 8, 43, 95, 40, 44, 92, 90, 95, 59, 28, 74, 17, 47, 81, 91, 33, 95, 6, 53, 13, 48, 34, 32, 58, 95, 49, 0, 19, 13, 54, 37, 8, 70, 91, 73, 69, 28, 90, 57, 95, 26, 14, 34, 52, 13, 92, 87, 95, 87, 54, 59, 95, 92, 69, 90, 21, 33, 90, 0, 93, 70, 90, 29, 95, 17, 90, 9, 27, 92, 87, 26, 36, 74, 9, 63, 3, 20, 17, 95, 95, 65, 45, 92, 48, 55, 19, 69, 60, 90, 7, 73, 24, 19, 9, 47, 96, 71, 76, 17, 83, 95, 21, 60, 72, 57, 85, 73, 59, 6, 23, 55, 0, 67, 89, 75, 51, 53, 19, 95, 55, 57, 71, 20, 54, 65, 66, 83, 1, 95, 89, 90, 60, 63, 63, 85, 13, 53, 55, 70, 29, 78, 63, 8, 23, 22, 96, 90, 3, 92, 74, 63, 13, 55, 10, 81, 46, 93, 95, 72, 59, 65, 34, 85, 36, 3, 47, 8, 64, 74, 55, 66, 28, 27, 21, 54, 33, 73, 70, 18, 22, 5, 43, 43, 13, 5, 41, 39, 89, 56, 43, 9, 96, 78, 37, 53, 34, 43, 0, 88, 51, 61, 0, 69, 73, 32, 55, 56, 18, 39, 54, 93, 33, 15, 69, 82, 24, 59, 21, 31, 49, 22, 60, 22, 86, 47, 65, 63, 73, 66, 32, 39, 38, 48, 14, 52, 88, 96, 90, 7, 77, 28, 63, 62, 42, 9, 37, 59, 65, 71, 0, 51, 9, 59, 8, 70, 68, 26, 60, 90, 60, 0, 90, 25, 51, 9, 90, 25, 91, 54, 77, 43, 75, 14, 59, 85, 87, 33, 13, 17, 81, 0, 64, 17, 52, 40, 91, 37, 60, 25]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vc6W07QVYoaM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Pre-process the data as before by making sure it's float32 and normalized between 0 and 1."
      ]
    },
    {
      "metadata": {
        "id": "qnXaiAgJYoaQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4f20cdc6-c6a4-4f88-e45f-e7fbedfcdc55"
      },
      "cell_type": "code",
      "source": [
        "# normalize data\n",
        "x_train = x_train.astype('float32') / 255.\n",
        "x_val = x_val.astype('float32') / 255.\n",
        "x_test = x_test.astype('float32') / 255.\n",
        "\n",
        "# convert labels to one-hot vectors\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_val = keras.utils.to_categorical(y_val, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(932, 97)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ordUucUKYoaS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's get a summary of what we have."
      ]
    },
    {
      "metadata": {
        "id": "AcKjxgtyYoaT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "7b8456a1-472b-45ce-818f-3b0f670aac94"
      },
      "cell_type": "code",
      "source": [
        "# summary\n",
        "print(\"finished loading %d images from %d categories\"%(len(data), num_classes))\n",
        "print(\"train / validation / test split: %d, %d, %d\"%(len(x_train), len(x_val), len(x_test)))\n",
        "print(\"training data shape: \", x_train.shape)\n",
        "print(\"training labels shape: \", y_train.shape)\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "finished loading 6209 images from 97 categories\n",
            "train / validation / test split: 4346, 931, 932\n",
            "training data shape:  (4346, 224, 224, 3)\n",
            "training labels shape:  (4346, 97)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "E-P9MNPcYoaY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "If everything worked properly, you should have loaded a bunch of images, and split them into three sets: `train`, `val`, and `test`. The shape of the training data should be (`n`, 224, 224, 3) where `n` is the size of your training set, and the labels should be (`n`, `c`) where `c` is the number of classes (97 in the case of `101_ObjectCategories`. \n",
        "\n",
        "Notice that we divided all the data into three subsets -- a training set `train`, a validation set `val`, and a test set `test`. The reason for this is to properly evaluate the accuracy of our classifier. During training, the optimizer uses the validation set to evaluate its internal performance, in order to determine the gradient without overfitting to the training set. The `test` set is always held out from the training algorithm, and is only used at the end to evaluate the final accuracy of our model.\n",
        "\n",
        "Let's quickly look at a few sample images from our dataset."
      ]
    },
    {
      "metadata": {
        "id": "y84SmM2CYoaZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "outputId": "37c0137b-d993-4b2d-fa4b-51fe368e8fa1"
      },
      "cell_type": "code",
      "source": [
        "images = [os.path.join(dp, f) for dp, dn, filenames in os.walk(root) for f in filenames if os.path.splitext(f)[1].lower() in ['.jpg','.png','.jpeg']]\n",
        "idx = [int(len(images) * random.random()) for i in range(8)]\n",
        "imgs = [image.load_img(images[i], target_size=(224, 224)) for i in idx]\n",
        "concat_image = np.concatenate([np.asarray(img) for img in imgs], axis=1)\n",
        "plt.figure(figsize=(16,4))\n",
        "plt.imshow(concat_image)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f233bce49b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7pEQ1tZqmxEdrjZAGpSKCICCTyVjSR0wQBE0SGcUCN+uiY00jaFhLHPZdTcmVkU7zf6U0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\nyJ2iJ2k06rSadVQFoiBEetByHBzfJwglnldDoMdV6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w3cc88B9r++i9//4OUcOHCAcsFB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\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Wn7GGZCrLwb37SBlRsdvd3UlMArtaYapYpK+3l3KpQEdXJ5Ztk0klouag8Ak8i1JpDsey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\nGo8tN2bDT/8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f2340b7bba8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "n2s5qypkYoad",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### First training a neural net from scratch\n",
        "\n",
        "Before doing the transfer learning, let's first build a neural network from scratch for doing classification on our dataset. This will give us a baseline to compare to our transfer-learned network later.\n",
        "\n",
        "The network we will construct contains 4 alternating convolutional and max-pooling layers, followed by a [dropout](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf) after every other conv/pooling pair. After the last pooling layer, we will attach a fully-connected layer with 256 neurons, another dropout layer, then finally a softmax classification layer for our classes.\n",
        "\n",
        "Our loss function will be, as usual, categorical cross-entropy loss, and our learning algorithm will be [AdaDelta](https://arxiv.org/abs/1212.5701). Various things about this network can be changed to get better performance, perhaps using a larger network or a different optimizer will help, but for the purposes of this notebook, the goal is to just get an understanding of an approximate baseline for comparison's sake, and so it isn't neccessary to spend much time trying to optimize this network.\n",
        "\n",
        "Upon compiling the network, let's run `model.summary()` to get a snapshot of its layers."
      ]
    },
    {
      "metadata": {
        "id": "y41GiiYTYoaf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "outputId": "02570f8a-c565-4aee-e299-5af6f464377d"
      },
      "cell_type": "code",
      "source": [
        "# build the network\n",
        "model = Sequential()\n",
        "print(\"Input dimensions: \",x_train.shape[1:])\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), input_shape=x_train.shape[1:]))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256))\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(num_classes))\n",
        "model.add(Activation('softmax'))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input dimensions:  (224, 224, 3)\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_17 (Conv2D)           (None, 222, 222, 32)      896       \n",
            "_________________________________________________________________\n",
            "activation_25 (Activation)   (None, 222, 222, 32)      0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_17 (MaxPooling (None, 111, 111, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_18 (Conv2D)           (None, 109, 109, 32)      9248      \n",
            "_________________________________________________________________\n",
            "activation_26 (Activation)   (None, 109, 109, 32)      0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_18 (MaxPooling (None, 54, 54, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_13 (Dropout)         (None, 54, 54, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_19 (Conv2D)           (None, 52, 52, 32)        9248      \n",
            "_________________________________________________________________\n",
            "activation_27 (Activation)   (None, 52, 52, 32)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_19 (MaxPooling (None, 26, 26, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 24, 24, 32)        9248      \n",
            "_________________________________________________________________\n",
            "activation_28 (Activation)   (None, 24, 24, 32)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_20 (MaxPooling (None, 12, 12, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_14 (Dropout)         (None, 12, 12, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 256)               1179904   \n",
            "_________________________________________________________________\n",
            "activation_29 (Activation)   (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dropout_15 (Dropout)         (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 97)                24929     \n",
            "_________________________________________________________________\n",
            "activation_30 (Activation)   (None, 97)                0         \n",
            "=================================================================\n",
            "Total params: 1,233,473\n",
            "Trainable params: 1,233,473\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ej9IWCzxYoai",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We've created a medium-sized network with ~1.2 million weights and biases (the parameters). Most of them are leading into the one pre-softmax fully-connected layer \"dense_5\".\n",
        "\n",
        "We can now go ahead and train our model for 100 epochs with a batch size of 128. We'll also record its history so we can plot the loss over time later. "
      ]
    },
    {
      "metadata": {
        "id": "CIqHecNAYoaj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "6456ff7a-34b7-4d23-ac2f-742b2dbdece4"
      },
      "cell_type": "code",
      "source": [
        "# compile the model to use categorical cross-entropy loss function and adadelta optimizer\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(x_train, y_train,\n",
        "                    batch_size=128,\n",
        "                    epochs=10,\n",
        "                    validation_data=(x_val, y_val))\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 4346 samples, validate on 931 samples\n",
            "Epoch 1/10\n",
            "4346/4346 [==============================] - 11s 3ms/step - loss: 4.5153 - acc: 0.0403 - val_loss: 4.4140 - val_acc: 0.0763\n",
            "Epoch 2/10\n",
            "4346/4346 [==============================] - 10s 2ms/step - loss: 4.2447 - acc: 0.1010 - val_loss: 4.1066 - val_acc: 0.1407\n",
            "Epoch 3/10\n",
            "4346/4346 [==============================] - 10s 2ms/step - loss: 3.9434 - acc: 0.1519 - val_loss: 3.7939 - val_acc: 0.2052\n",
            "Epoch 4/10\n",
            "4346/4346 [==============================] - 10s 2ms/step - loss: 3.6178 - acc: 0.2046 - val_loss: 3.5077 - val_acc: 0.2309\n",
            "Epoch 5/10\n",
            "4346/4346 [==============================] - 10s 2ms/step - loss: 3.3217 - acc: 0.2531 - val_loss: 3.3025 - val_acc: 0.2696\n",
            "Epoch 6/10\n",
            "4346/4346 [==============================] - 10s 2ms/step - loss: 3.0783 - acc: 0.2931 - val_loss: 3.0644 - val_acc: 0.3083\n",
            "Epoch 7/10\n",
            "4346/4346 [==============================] - 10s 2ms/step - loss: 2.7861 - acc: 0.3502 - val_loss: 2.8714 - val_acc: 0.3373\n",
            "Epoch 8/10\n",
            "4346/4346 [==============================] - 10s 2ms/step - loss: 2.5162 - acc: 0.3971 - val_loss: 2.7907 - val_acc: 0.3663\n",
            "Epoch 9/10\n",
            "4346/4346 [==============================] - 10s 2ms/step - loss: 2.2286 - acc: 0.4478 - val_loss: 2.7009 - val_acc: 0.3910\n",
            "Epoch 10/10\n",
            "4346/4346 [==============================] - 10s 2ms/step - loss: 2.0241 - acc: 0.4979 - val_loss: 2.6859 - val_acc: 0.3996\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yG0CKOI1Yoao",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's plot the validation loss and validation accuracy over time."
      ]
    },
    {
      "metadata": {
        "id": "_CCPq_ndYoap",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "99227211-9f16-4e5f-ceaa-68beb3817ad4"
      },
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(16,4))\n",
        "ax = fig.add_subplot(121)\n",
        "ax.plot(history.history[\"val_loss\"])\n",
        "ax.set_title(\"validation loss\")\n",
        "ax.set_xlabel(\"epochs\")\n",
        "\n",
        "ax2 = fig.add_subplot(122)\n",
        "ax2.plot(history.history[\"val_acc\"])\n",
        "ax2.set_title(\"validation accuracy\")\n",
        "ax2.set_xlabel(\"epochs\")\n",
        "ax2.set_ylim(0, 1)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pT5dbwkj7EuKi+ZcbJnHexGx27q/lx4tWUVp+ONRhiYiIiIiI9CtKSs9AVGQEN19SwLVz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\nSZIkSSrGolSSJEmSVIxFqSRJkiSpmP8DqI6Af6zY8G8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f2333a300f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "GI7Mj6-RYoau",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Notice that the validation loss begins to actually rise after around 16 epochs, even though validation accuracy remains roughly between 40% and 50%. This suggests our model begins overfitting around then, and best performance would have been achieved if we had stopped early around then. Nevertheless, our accuracy would not have likely been above 50%, and probably lower down.\n",
        "\n",
        "We can also get a final evaluation by running our model on the training set. Doing so, we get the following results:"
      ]
    },
    {
      "metadata": {
        "id": "8Itd5LDAYoav",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "97d9a1ed-e135-4b08-ca1c-9053758db9b1"
      },
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(x_test, y_test, verbose=0)\n",
        "print('Test loss:', loss)\n",
        "print('Test accuracy:', accuracy)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 2.6905115888865723\n",
            "Test accuracy: 0.4034334763948498\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vIwMY_ZXYoax",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Finally, we see that we have achieved a (top-1) accuracy of around 49%. That's not too bad for 6000 images, considering that if we were to use a naive strategy of taking random guesses, we would have only gotten around 1% accuracy. \n",
        "\n",
        "## Transfer learning by starting with existing network\n",
        "\n",
        "Now we can move on to the main strategy for training an image classifier on our small dataset: by starting with a larger and already trained network.\n",
        "\n",
        "To start, we will load the VGG16 from keras, which was trained on ImageNet and the weights saved online. If this is your first time loading VGG16, you'll need to wait a bit for the weights to download from the web. Once the network is loaded, we can again inspect the layers with the `summary()` method."
      ]
    },
    {
      "metadata": {
        "id": "KpUDAbxiYoay",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 952
        },
        "outputId": "ed9ec74b-6697-438b-8e82-61df8c9da39f"
      },
      "cell_type": "code",
      "source": [
        "vgg = keras.applications.VGG16(weights='imagenet', include_top=True)\n",
        "vgg.summary()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5\n",
            "553467904/553467096 [==============================] - 7s 0us/step\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              102764544 \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "predictions (Dense)          (None, 1000)              4097000   \n",
            "=================================================================\n",
            "Total params: 138,357,544\n",
            "Trainable params: 138,357,544\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "hLXTofcNYoa2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Notice that VGG16 is _much_ bigger than the network we constructed earlier. It contains 13 convolutional layers and two fully connected layers at the end, and has over 138 million parameters, around 100 times as many parameters than the network we made above. Like our first network, the majority of the parameters are stored in the connections leading into the first fully-connected layer.\n",
        "\n",
        "VGG16 was made to solve ImageNet, and achieves a [8.8% top-5 error rate](https://github.com/jcjohnson/cnn-benchmarks), which means that 91.2% of test samples were classified correctly within the top 5 predictions for each image. It's top-1 accuracy--equivalent to the accuracy metric we've been using (that the top prediction is correct)--is 73%. This is especially impressive since there are not just 97, but 1000 classes, meaning that random guesses would get us only 0.1% accuracy.\n",
        "\n",
        "In order to use this network for our task, we \"remove\" the final classification layer, the 1000-neuron softmax layer at the end, which corresponds to ImageNet, and instead replace it with a new softmax layer for our dataset, which contains 97 neurons in the case of the 101_ObjectCategories dataset. \n",
        "\n",
        "In terms of implementation, it's easier to simply create a copy of VGG from its input layer until the second to last layer, and then work with that, rather than modifying the VGG object directly. So technically we never \"remove\" anything, we just circumvent/ignore it. This can be done in the following way, by using the keras `Model` class to initialize a new model whose input layer is the same as VGG but whose output layer is our new softmax layer, called `new_classification_layer`. Note: although it appears we are duplicating this large network, internally Keras is actually just copying all the layers by reference, and thus we don't need to worry about overloading the memory."
      ]
    },
    {
      "metadata": {
        "id": "rFL-fLitYoa3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# make a reference to VGG's input layer\n",
        "inp = vgg.input\n",
        "\n",
        "# make a new softmax layer with num_classes neurons\n",
        "new_classification_layer = Dense(num_classes, activation='softmax')\n",
        "\n",
        "# connect our new layer to the second to last layer in VGG, and make a reference to it\n",
        "out = new_classification_layer(vgg.layers[-2].output)\n",
        "\n",
        "# create a new network between inp and out\n",
        "model_new = Model(inp, out)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MBIp3fbQYoa9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We are going to retrain this network, `model_new` on the new dataset and labels. But first, we need to freeze the weights and biases in all the layers in the network, except our new one at the end, with the expectation that the features that were learned in VGG should still be fairly relevant to the new image classification task. Not optimal, but most likely better than what we can train to in our limited dataset. \n",
        "\n",
        "By setting the `trainable` flag in each layer false (except our new classification layer), we ensure all the weights and biases in those layers remain fixed, and we simply train the weights in the one layer at the end. In some cases, it is desirable to *not* freeze all the pre-classification layers. If your dataset has enough samples, and doesn't resemble ImageNet very much, it might be advantageous to fine-tune some of the VGG layers along with the new classifier, or possibly even all of them. To do this, you can change the below code to make more of the layers trainable.\n",
        "\n",
        "In the case of CalTech-101, we will just do feature extraction, fearing that fine-tuning too much with this dataset may overfit. But maybe we are wrong? A good exercise would be to try out both, and compare the results.\n",
        "\n",
        "So we go ahead and freeze the layers, and compile the new model with exactly the same optimizer and loss function as in our first network, for the sake of a fair comparison. We then run `summary` again to look at the network's architecture."
      ]
    },
    {
      "metadata": {
        "id": "e_n5A8oGYoa9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 918
        },
        "outputId": "ebe81af3-f953-4351-ef10-5b63f3d19d75"
      },
      "cell_type": "code",
      "source": [
        "# make all layers untrainable by freezing weights (except for last layer)\n",
        "for l, layer in enumerate(model_new.layers[:-1]):\n",
        "    layer.trainable = False\n",
        "\n",
        "# ensure the last layer is trainable/not frozen\n",
        "for l, layer in enumerate(model_new.layers[-1:]):\n",
        "    layer.trainable = True\n",
        "\n",
        "model_new.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model_new.summary()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              102764544 \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 97)                397409    \n",
            "=================================================================\n",
            "Total params: 134,657,953\n",
            "Trainable params: 397,409\n",
            "Non-trainable params: 134,260,544\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8B9G0gC1YobD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Looking at the summary, we see the network is identical to the VGG model we instantiated earlier, except the last layer, formerly a 1000-neuron softmax, has been replaced by a new 97-neuron softmax. Additionally, we still have roughly 134 million weights, but now the vast majority of them are \"non-trainable params\" because we froze the layers they are contained in. We now only have 397,000 trainable parameters, which is actually only a quarter of the number of parameters needed to train the first model.\n",
        "\n",
        "As before, we go ahead and train the new model, using the same hyperparameters (batch size and number of epochs) as before, along with the same optimization algorithm. We also keep track of its history as we go."
      ]
    },
    {
      "metadata": {
        "id": "aDdq71XNYobD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "907479a2-3a12-44b7-b706-3f86e3a4ea26"
      },
      "cell_type": "code",
      "source": [
        "history2 = model_new.fit(x_train, y_train, \n",
        "                         batch_size=128, \n",
        "                         epochs=10, \n",
        "                         validation_data=(x_val, y_val))\n"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 4346 samples, validate on 931 samples\n",
            "Epoch 1/10\n",
            "4346/4346 [==============================] - 66s 15ms/step - loss: 4.0574 - acc: 0.1631 - val_loss: 3.1996 - val_acc: 0.3040\n",
            "Epoch 2/10\n",
            "4346/4346 [==============================] - 46s 11ms/step - loss: 2.5812 - acc: 0.4441 - val_loss: 2.3154 - val_acc: 0.5070\n",
            "Epoch 3/10\n",
            "4346/4346 [==============================] - 46s 11ms/step - loss: 1.9747 - acc: 0.5630 - val_loss: 1.9155 - val_acc: 0.5585\n",
            "Epoch 4/10\n",
            "4346/4346 [==============================] - 46s 11ms/step - loss: 1.6366 - acc: 0.6305 - val_loss: 1.7621 - val_acc: 0.5768\n",
            "Epoch 5/10\n",
            "4346/4346 [==============================] - 46s 11ms/step - loss: 1.4263 - acc: 0.6795 - val_loss: 1.5046 - val_acc: 0.6681\n",
            "Epoch 6/10\n",
            "4346/4346 [==============================] - 46s 11ms/step - loss: 1.2610 - acc: 0.7098 - val_loss: 1.4539 - val_acc: 0.6477\n",
            "Epoch 7/10\n",
            "4346/4346 [==============================] - 46s 11ms/step - loss: 1.1283 - acc: 0.7407 - val_loss: 1.3778 - val_acc: 0.6778\n",
            "Epoch 8/10\n",
            "4346/4346 [==============================] - 46s 11ms/step - loss: 1.0551 - acc: 0.7508 - val_loss: 1.2849 - val_acc: 0.7014\n",
            "Epoch 9/10\n",
            "4346/4346 [==============================] - 46s 11ms/step - loss: 0.9447 - acc: 0.7862 - val_loss: 1.2487 - val_acc: 0.6971\n",
            "Epoch 10/10\n",
            "4346/4346 [==============================] - 46s 11ms/step - loss: 0.8976 - acc: 0.7931 - val_loss: 1.2612 - val_acc: 0.6853\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jPqJ0OM8YobI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Our validation accuracy hovers close to 80% towards the end, which is more than 30% improvement on the original network trained from scratch (meaning that we make the wrong prediction on 20% of samples, rather than 50%). \n",
        "\n",
        "It's worth noting also that this network actually trains _slightly faster_ than the original network, despite having more than 100 times as many parameters! This is because freezing the weights negates the need to backpropagate through all those layers, saving us on runtime.\n",
        "\n",
        "Let's plot the validation loss and accuracy again, this time comparing the original model trained from scratch (in blue) and the new transfer-learned model in green."
      ]
    },
    {
      "metadata": {
        "id": "SHLdHnuuYobJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "bd5a066f-c94c-4ac4-adc5-3154e6143a29"
      },
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(16,4))\n",
        "ax = fig.add_subplot(121)\n",
        "ax.plot(history.history[\"val_loss\"])\n",
        "ax.plot(history2.history[\"val_loss\"])\n",
        "ax.set_title(\"validation loss\")\n",
        "ax.set_xlabel(\"epochs\")\n",
        "\n",
        "ax2 = fig.add_subplot(122)\n",
        "ax2.plot(history.history[\"val_acc\"])\n",
        "ax2.plot(history2.history[\"val_acc\"])\n",
        "ax2.set_title(\"validation accuracy\")\n",
        "ax2.set_xlabel(\"epochs\")\n",
        "ax2.set_ylim(0, 1)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ERGR4Gmja+Vegro/tPwDuAq4FFhpjJkSqsOFs9KgkvvSJK/jWIzOZPTGbozXN/PIvO/mX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\nyGD+EkcTgkkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f2332f62b00>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "vXjfVTVIYobM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Notice that whereas the original model began overfitting around epoch 16, the new model continued to slowly decrease its loss over time, and likely would have improved its accuracy slightly with more iterations. The new model made it to roughly 80% top-1 accuracy (in the validation set) and continued to improve slowly through 100 epochs.\n",
        "\n",
        "It's possibly we could have improved the original model with better regularization or more dropout, but we surely would not have made up the >30% improvement in accuracy. \n",
        "\n",
        "Again, we do a final validation on the test set."
      ]
    },
    {
      "metadata": {
        "id": "zMxC6Pd1YobN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "944eede7-79a7-42b3-ba26-bd883c2e7b46"
      },
      "cell_type": "code",
      "source": [
        "loss, accuracy = model_new.evaluate(x_test, y_test, verbose=0)\n",
        "\n",
        "print('Test loss:', loss)\n",
        "print('Test accuracy:', accuracy)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 1.1540323304004423\n",
            "Test accuracy: 0.7156652360515021\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4iUykardYobR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "To predict a new image, simply run the following code to get the probabilities for each class."
      ]
    },
    {
      "metadata": {
        "id": "YpRcsywEYobT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "858eb7b8-f403-451c-83b9-7eda70671cfc"
      },
      "cell_type": "code",
      "source": [
        "img, x = get_image('101_ObjectCategories/airplanes/image_0003.jpg')\n",
        "probabilities = model_new.predict([x])\n"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6425913\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "f2ahKv8XYobY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Improving the results\n",
        "\n",
        "78.2% top-1 accuracy on 97 classes, roughly evenly distributed, is a pretty good achievement. It is not quite as impressive as the original VGG16 which achieved 73% top-1 accuracy on 1000 classes. Nevertheless, it is much better than what we were able to achieve with our original network, and there is room for improvement. Some techniques which possibly could have improved our performance.\n",
        "\n",
        "- Using data augementation: augmentation refers to using various modifications of the original training data, in the form of distortions, rotations, rescalings, lighting changes, etc to increase the size of the training set and create more tolerance for such distortions.\n",
        "- Using a different optimizer, adding more regularization/dropout, and other hyperparameters.\n",
        "- Training for longer (of course)\n",
        "\n",
        "A more advanced example of transfer learning in Keras, involving augmentation for a small 2-class dataset, can be found in the [Keras blog](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)."
      ]
    }
  ]
}